Version 1
: Received: 26 February 2019 / Approved: 27 February 2019 / Online: 27 February 2019 (12:01:41 CET)
How to cite:
Zhao, L.; Liu, Z.; Mbachu, J. Bayesian Structural Equation Modeling for Examining the Relationship between Multidimensional Factors and Construction Project Cost. Preprints2019, 2019020255. https://doi.org/10.20944/preprints201902.0255.v1
Zhao, L.; Liu, Z.; Mbachu, J. Bayesian Structural Equation Modeling for Examining the Relationship between Multidimensional Factors and Construction Project Cost. Preprints 2019, 2019020255. https://doi.org/10.20944/preprints201902.0255.v1
Zhao, L.; Liu, Z.; Mbachu, J. Bayesian Structural Equation Modeling for Examining the Relationship between Multidimensional Factors and Construction Project Cost. Preprints2019, 2019020255. https://doi.org/10.20944/preprints201902.0255.v1
APA Style
Zhao, L., Liu, Z., & Mbachu, J. (2019). Bayesian Structural Equation Modeling for Examining the Relationship between Multidimensional Factors and Construction Project Cost. Preprints. https://doi.org/10.20944/preprints201902.0255.v1
Chicago/Turabian Style
Zhao, L., Zhansheng Liu and Jasper Mbachu. 2019 "Bayesian Structural Equation Modeling for Examining the Relationship between Multidimensional Factors and Construction Project Cost" Preprints. https://doi.org/10.20944/preprints201902.0255.v1
Abstract
Construction projects are usually operating in a complex and dynamic environment in which the accumulation of many interrelated factors causes high uncertainty. Construction projects are complex and frequently involve substantial uncertainties including process complicatedness, intricate organization structure, dynamic environment, and financial strain. The study aims to categorize the influencing factors into three groups, namely construction project system, economic-market climate, and external environment. It attempts to adopt a novel analysis tool to examine the relationship between the project cost and multiple influencing factors by using Bayesian SEM. While the Bayesian SEM method has been receiving increasing attention in exploring the relationship between latent variables, construction studies still heavily rely on the covariance-based SEM approach. This study introduces several advantages of Bayesian SEM that make it more flexible and powerful than covariance-based SEM and provides the foundation of Bayesian SEM estimation and inference by illustrating this method in a project cost application.
Keywords
Construction project cost; influencing factors; Bayesian SEM; New Zealand
Subject
Engineering, Civil Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
